Methods and systems for resource planning in a shared spectra

ABSTRACT

Techniques for modelling a radio network in a geographic region utilizing shared spectra are disclosed. Population data is obtained for the geographic region. A number of radios per channel in the geographic region, N′, is determined. Candidate geographic location for radios in the geographic region are determined. For every channel in the shared spectra, at least one of a static dataset and a dynamic dataset is determined. At least one set of output data, that is a statistical characterization of the radio network, is generated using at least one of the static dataset and the dynamic dataset, to aid in design of the radio network.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims benefit of U.S. Patent Application Ser.No. 62/827,460, filed Apr. 1, 2019; the entire contents of theaforementioned patent application are incorporated herein by referenceas if set forth in its entirety.

BACKGROUND

Communications networks (such as a typical cellular telephone network)utilizing conventional licensed frequency spectra (or spectra) aredesigned using network planning tools to ensure satisfactory operation.In such conventional licensed frequency spectra, frequency bands aredivided among wireless service providers with typically one provideroccupying a given frequency band. Because any given frequency band (orspectrum) is utilized by a single network operator, the network operatorhas full control of the information to model a communications networkoperating in a corresponding frequency band. Thus, modelling such anenvironment is relatively easy.

Spectra usage shared by governmental and commercial users has beenproposed, and is expected to be deployed in the future. A multitude ofnetwork operator and other users of the shared spectra, known and/orunknown, to a network operator would utilize such shared spectra. Forexample, the shared spectra usage may dynamically vary by geographicregion, frequency band, and time—for reasons outside a networkoperator's control. As a result, an operator does not have comparablecontrol of the information to model communications networks operating inthe shared spectra, making network planning in shared spectrasignificantly more complex. Therefore, there is a need for a networkplanning and optimization tool to design communications networks forsuch shared spectra.

SUMMARY OF THE INVENTION

A method of modelling a radio network in a geographic region utilizingshared spectra is provided. The method comprises: obtaining populationdata for the geographic region; determining a number of radios perchannel in the geographic region, N′; determining candidate geographiclocation for radios in the geographic region; determining, for everychannel in the shared spectra, at least one of a static dataset and adynamic dataset; wherein the static dataset comprises a set of n*N′*Relements, where n is a number of data types, comprising at least oneindependent variable and at least one dependent variable, of the staticdataset and R is a number of trials to determine the at least onedependent variable using random values of the at least one independentvariable, where the data types comprise a first set of radio parameterswhose values are randomly selected and a radio transmit power level fora channel, and where the radio transmit power level for the channel isdetermined so as to distribute interference margin amongst radios in aneighborhood of a protection point or an incumbent user using therandomly selected values of the first set of radio parameters; whereinthe dynamic dataset comprises a set of l*N′*R elements, where l is anumber of data types, comprising at least one independent variable andat least one dependent variable, of the dynamic data set, where the datatypes comprise a second set of radio parameters whose values arerandomly selected and a move list of radios, operating in the channel,whose transmissions are terminated upon operation of an incumbent systemusing the channel, and where the move list is determined with therandomly selected values of the second set of radio parameters; andgenerating at least one set of output data that is a statisticalcharacterization of the radio network, using at least one of the staticdataset and the dynamic dataset, to determine at least one of spectrumavailability over all geographic regions and likelihood of a radio beingplaced on a move list over all geographic regions.

DRAWINGS

FIG. 1 illustrates one embodiment of a method of modelling acommunications network utilizing shared spectra; and

FIG. 2 illustrates one embodiment of a shared spectra radio networkplanning system.

DETAILED DESCRIPTION

Techniques for modelling a shared spectrum environment will besubsequently described. The illustrated techniques improve radio networkdesign systems so that they can model radio systems using sharedspectra. First, exemplary shared spectrum technology will be discussed.

An example of a shared access system is the proposed Citizens BroadbandRadio Service (CBRS) specified by the United States FederalCommunications Commission (FCC). A comparable shared spectrumtechnology, Licensed Shared Access (LSA), has been proposed in Europe.However, the techniques proposed herein are applicable to any type ofshared access system. Further, wherever the term citizens broadbandradio service device (CBSD) is used hereafter, it is an example of aradio frequency (RF) access system, or more generally a radio. In oneembodiment, an RF access system is a base station, access point, or anyother type of radio. The term “channel” may be used to describe afrequency channel or spectrum. The channel, for example, may be 5 or 10MHz in bandwidth.

A CBRS will initially be described. However, the invention will besubsequently described in more general terms, e.g., using the term radiorather than CBSD of an CBRS.

A CBRS comprises general authorized access (GAA) and/or priority accesslicense (PAL) CBSDs and higher priority users, e.g., incumbent users.The terms system may be used interchangeably with the term user, andshall have the same meaning; thus, e.g., incumbent user means isequivalent to incumbent system. The higher priority users, such asgovernment users for example radar systems, e.g., on ships, havepriority access to certain spectrum in the shared spectra. A SAScontroller grants the CBSDs access to the shared spectra, includingassigning frequency spectrum (or channels) and optionally maximumtransmission power. A SAS controller controls the transmission of GAACBSDs so that they do not interfere with PAL CB SDs and the higherpriority users. The SAS controller also controls the transmission of PALCBSDs so that they do not interfere with the higher priority users.

GAA CBSDs may be of two types: category A (low power) and category B(high power). Category A has a maximum transmission power spectraldensity of 30 dBm/10 MHz. Category B has a maximum transmission powerspectral density of 47 dBm/10 MHz. Power or power level as used hereinincludes power spectral density.

Incumbent users of shared spectra have first, or highest, priority toutilize the shared spectra controlled by the SAS controller. Thus,incumbent users shall be able to operate free of interference from otherusers, e.g., communications systems of priority access licensees andgeneral authorized access users. Free of interference as used hereindoes not mean an absence of interference, but rather means an acceptablelevel of interference which may be no interference or a finite level ofinterference. The acceptable level of interference may vary bygeography, frequency spectrum, user type, license type, and/or otherindicia. In one embodiment, the incumbent users include governmententities operating systems such as communications systems, operators offixed satellite communications systems, and grandfathered, priorlicensees of the spectrum. Communications systems, as used herein, shallinclude radar systems (or radars).

In one embodiment, PAL users have second (or intermediate) priority,after incumbent users, to utilize the frequency spectrum controlled bythe SAS controller. In another embodiment, a PAL user shall be able tooperate, when incumbent users are free of interference of such a PALuser, free of interference from other priority access licensees andgeneral authorized access users. In one embodiment, an ability of a PALuser to operate free of interference shall be limited temporally,geographically, and spectrally within the specifications of its license.

GAA users have third, or lowest, priority to utilize the frequencyspectrum controlled by the CBRS. In one embodiment, an operation of GAAusers will be governed by laws, regulations, and/or rules pertaining tothe CBRS, e.g., established by governmental(s) and/or standards bodies.For example, such rules shall only let GAA users' CB SDs operate whenthey do not interfere with communication systems of incumbent and PALusers. Also, for example, such rules shall only let a GAA user's CBSDsoperate as long as it does not interfere with another GAA user's CBSDauthorized to operate at the same time in the shared frequency spectrumcontrolled by the SAS controller. As will be subsequently discussed,some GAA users may have priority over other GAA users.

In one embodiment, the geographic coverage area proximate to (e.g.,covered by radio frequency emissions of) the CBSD may include exclusionzones and protection zones (including location(s) of fixed satelliteservice(s) (FSS(s)), priority access license (PAL) protection area(s)(PPA(s)), grandfathered wireless protection zone(s) (GWPZ(s)), andenvironmental sensing capability (ESC) system(s)). CBSDs are prohibitedfrom operating in specific frequency spectrum in exclusion zones.Further, the level of interference generated by, e.g., by allnon-government users and even some government users (includingincumbent, PAL, and GAA users) shall be limited in a protection zone soas not to interfere with certain incumbent user(s)′ communicationssystems, for example naval radar on ships, intended to be protected bythe protection zone. CBSDs may only operate with the permission of theSAS controller when an incumbent user's communication system isoperating in a protection zone. In some cases, this operation will bebased upon information received by an environmental sensing capabilitysystem, from external database(s), notification from an incumbent user,and/or from a beacon (which will be subsequently described). One type ofprotection zone is the grandfathered wireless protection zone which is ageographic area and/or frequency spectrum where grandfathered wirelessbroadband licensees can operate free of interference, e.g., of CB SDs.The foregoing are examples of exclusion zones and protection zones;other type of exclusion and protection zones may occur.

Prioritization of communications systems utilizing shared spectra hasbeen exemplified above in detail. However, embodiments of the inventioncan be implemented when no prioritization is utilized for communicationssystems sharing spectra.

Embodiments of the invention will now be described. Such embodimentsaddress the above cited problem by statistically characterizing, forexample determining at least one of a probability, mean, or median ofspectrum availability in channels of the shared spectra in thegeographic region. Thus, statistical characterization means determininga probability of occurrence, a mean, or a median of a variable over aset of trials. Such statistical characterization may be performed, e.g.,by running Monte Carlo simulations of radio deployment scenarios, usingpredictive modeling of shared spectra over a geographic region(s). Thestatistical characterization is determined for at least one of:

-   (a) spectrum availability in channels of the shared spectra in the    geographic region;-   (b) desirable radio parameters for radios operating in a channel and    at a geographic location;-   (c) for a given channel and geographic location, a radio at a    geographic location and operating on a given channel will be on a    move list;-   (d) for a given channel, geographic locations requiring a priority    access license due to proximity of other radios; and-   (e) for a given channel, geographic locations where radios require a    backup channel to maintain continuity of service. Embodiments of the    invention can be used to perform such statistical characterization    as market penetration increases, e.g., to aid in planning and    determining deployment as the spectra is shared amongst a growing    number of users. Market penetration represents a percentage of users    (e.g., end user devices utilizing GAA CBSDs) of the corresponding    shared spectra as a percentage of the population of the    corresponding geographic region being analyzed.

FIG. 1 illustrates one embodiment of a method 100 of modelling acommunications network utilizing shared spectra. A communication networkas used herein means a network of one or more radios.

In block 102, obtain population data for a geographic region.Optionally, obtain population data for geographic regions from anexternal database, e.g., from a national governmental entity (forexample the U.S. Census Bureau), a commercial, and/or anothernon-commercial source. Optionally, obtain additionally data for thegeographic region such a map data and/or geographic morphology data.

Optionally, the geographic region may comprise one or more largergeographic regions, e.g., counties, towns, and/or cities). Thepopulation data for the geographic region may be comprised of populationdata broken down by the one or more larger geographic regions.Optionally, a larger geographic region may comprise one or more smallergeographic regions, e.g., blocks and/or tracts). The population data fora larger geographic region may be comprised of population data brokendown by the one or more smaller geographic regions. Thus, optionally,obtaining the population for data for the geographic region comprisingobtaining data for at least one of: at least one larger geographicregion, and at least one smaller geographic region comprising one of thelarger geographic regions.

In block 104, determine a number of radios per channel in the geographicregion. In one embodiment, the number of radios, N, per geographicregion is determined where:

$\begin{matrix}{N = \frac{{Population}\mspace{14mu} {of}\mspace{14mu} {Geographic}\mspace{14mu} {Region}*{Market}\mspace{14mu} {Penetration}\mspace{14mu} {factor}*{Channel}\mspace{14mu} {Factor}}{{Number}\mspace{14mu} {of}\mspace{14mu} {{Users}/{Radio}}}} & \left( {{Equation}\mspace{14mu} 1} \right)\end{matrix}$

If the number of radios per geographic region is a non-integer number,then it is rounded up to ensure that satisfactory network capacity isavailable for end users, e.g., user equipment that communicate with theradio(s). Other equations for determining the number of radios pergeographic region are found in Drocella et al., 3.5 GHz Exclusion ZoneAnalyses and Methodology, U.S. Department of Commerce, NationalTelecommunications and Information Administration (NTIA) Report 15-517,2015-16; the contents of the NTIA Report 15-517 are incorporated byreference herein in its entirety.

The number of users per radio may be determined by a modelling systemdesigner and/or user and will vary based upon geographic morphology(urban, suburban, rural, etc.); the number of users per radio is anestimated number of user equipment served per radio. Typically, higherpower radios, e.g., Category B, will serve more users (or userequipment) than lower power radios.

Market penetration factor is a scaling factor representing a percentageof users (e.g., end user devices utilizing GAA CBSDs) of thecorresponding shared spectra as a percentage of the population of thecorresponding geographic region being analyzed. The market penetrationfactor can be varied by the modelling system user, and ranges betweenzero and a positive number, for example one. The channel factor is ascaling factor representing the number of radios expected to share achannel in a geographic region. The channel factor, for example, rangesfrom zero to one. Further, for example, if there are X radios usingchannels in shared spectra having a bandwidth BW_(SS) and each channelhas a bandwidth BW_(CH), then the number of radios sharing a givenchannel is (BW_(CH)/BW_(SS))*X. The channel factor can be varied by themodelling system user. In other embodiments, other equations fordetermining the number of radios can be used; for example, a subset ofthe aforementioned scaling factors may be used. Alternatively, otherscaling factors may be used in addition to and/or in lieu of the abovedescribed scaling factors.

Determining the number of radios in a geographic region as a whole maynot accurately predict locations of radios in the geographic region.Some portions of the geographic region may have higher populationdensities than others, and thus would require a higher density ofradios. Thus, in another embodiment, the number of radios in each largergeographic region forming the geographic region may be determined. Thenumber of radios, N_(Larger Geo. Region), per channel per largergeographic region may be determined by:

$\begin{matrix}{N_{{Larger}\mspace{14mu} {{Geo}.\mspace{14mu} {Region}}} = \frac{{Population}\mspace{14mu} {of}\mspace{14mu} {Larger}\mspace{14mu} {Geographic}\mspace{14mu} {Region}*{Market}\mspace{14mu} {Penetration}\mspace{14mu} {factor}*{Channel}\mspace{14mu} {Factor}}{{Number}\mspace{14mu} {of}\mspace{14mu} {{Users}/{Radio}}}} & \left( {{Equation}\mspace{14mu} 2} \right)\end{matrix}$

If the number of radios per larger geographic region is a non-integernumber, then it is rounded up to ensure that satisfactory networkcapacity is available for end users, e.g., user equipment thatcommunicate with the radio(s) in the smaller geographic region. Thetotal number of radios for the geographic region being analyzed isdetermined by summing the number of radios radio per larger geographicregion. Any rounding up is performed before or after such summation;however, performing the rounding up after the summation ensures thatthere will not be an excessive number of radios in the geographicregion.

Determining the number of radios in a larger geographic region as awhole may not accurately predict locations of radios in the geographicregion. Some portions of even the larger geographic region may havehigher population densities then others, and thus would require a higherdensity of radios. Thus, in a further embodiment, the number of radiosin each larger geographic region forming the geographic region may bedetermined. The number of radios, N_(Smaller Geo. Region), per channelper smaller geographic region may be determined by:

$\begin{matrix}{N_{{Smaller}\mspace{14mu} {{Geo}.\mspace{14mu} {Region}}} = \frac{{Population}\mspace{14mu} {of}\mspace{14mu} {Smaller}\mspace{14mu} {Geographic}\mspace{14mu} {Region}*{Market}\mspace{14mu} {Penetration}\mspace{14mu} {factor}*{Channel}\mspace{14mu} {Factor}}{{Number}\mspace{14mu} {of}\mspace{14mu} {{Users}/{Radio}}}} & \left( {{Equation}\mspace{14mu} 3} \right)\end{matrix}$

If the number of radios per smaller geographic region is a non-integernumber, then it may be rounded up (i.e., using a ceil mathematicalfunction) to ensure that satisfactory network capacity is available forend users, e.g., user equipment that communicate with the radio(s) inthe smaller geographic region. Whether the number of radios per smallergeographic region is rounded up is contingent upon population density.If the population density (e.g., the population per square kilometer orsquare mile) in the smaller geographic area is less than a populationdensity threshold level, then rounding up the number of radios computedfrom equation 3 will result in too many radios than necessary to servethe population in the small geographic area. On the other hand, if thepopulation density is greater than the population density thresholdlevel, then rounding up the number of radios will ensure that thesatisfactory network capacity is available for end users in the area.The population density threshold level is determined by implementers ofthe modelling system or radio system designers, e.g., of shared accesssystems. The total number of radios for the geographic region beinganalyzed is determined by summing the number of radios radio per smallergeographic region. Any rounding up is performed before or after suchsummation; however, performing the rounding up after the summationensures that there will not be an excessive number of radios in thegeographic region.

In yet another embodiment, the number of radios in a geographic regionmay be determined by determining both: the number of radios in largergeographic regions without determining the number of radios inconstituent smaller geographic regions; and the number of radios inlarger geographic regions by determining the number of radios inconstituent smaller geographic regions. This may be appropriate for ageographic region comprising larger geographic region(s) (e.g.,count(ies)) with a rural geographic morphology and uniform populationdistribution—for which the number of radios is determined based upondata pertaining to only the larger geographic region; and largergeographic region(s) (e.g., count(ies)) with a mixed geographicmorphology (e.g., rural and urban) and a non-uniform populationdistribution—for which the number of radios is determined based upondata pertaining to only the smaller geographic region. Thus, for examplein the manner described above, the number of radios is first determinedat smaller geographic region(s) for larger geographic region(s) havingnon-uniform population distributions (which alternatively could applyeven to non-mixed geographic morphology such as an urban morphology witha varying population density). The number of radios in the largergeographic regions is then determined by summing the number of radiosfor corresponding smaller geographic regions. Finally, the number ofradios for the geographic region is determined by summing the number ofradios for all larger geographic regions constituting the geographicregion.

In yet a further embodiment, the geographic region may comprise at leastone larger geographic region and at least one smaller geographic region(which is not a constituent of any of the at least one larger geographicregion). The number of radios in the geographic region is determined bysumming the number of radios in each of the at least one largergeographic region and the at least one smaller geographic region (whichis not a constituent of any of the at least one larger geographicregion)—for example as illustrated above.

In block 106, determine the candidate geographic locations for the Nradios in the geographic region. Candidate geographic location means apoint or an area. The candidate locations are constrained so that thedistance between radios exceeds the minimum inter-radio distance for theradio having the highest transmit power level of adjacent pairs ofradios. The minimum inter-radio distance, and thus any deploymentscaling factor, may vary based upon geographic morphology, and/or radiocategory type (e.g., maximum radio transmit power level). The minimuminter-radio distance—which can range from meters to kilometers—and/orany deployment scaling factor may be defined by the system user, systemdesigner, and/or automatically determined by the system.

The deployment scaling factor is a scaling factor whose unit iscandidate geographic locations per radio, and is a positive number whichis constrained to guarantee a minimum inter-radio distance (which variesby geographic morphology and the transmit power levels of radios thatcould be deployed in any of such smaller geographic regions). Theminimum inter-radio distance ensures a balance of radio network capacityand inter-radio interference. The deployment scaling factor ensures thatthe minimum inter-radio distance is satisfied to ensure a more realisticdistribution of radios in the analysis. CSGR is determined by roundingup or only using the integer of the resulting product (i.e., applyingthe floor mathematical function to the product).

The number of candidate geographic locations for each larger geographicregion formed by smaller geographic region(s) is the sum of thecandidate geographic locations for the corresponding smaller geographicregion(s) forming the larger geographic region. The number of candidategeographic locations in the geographic regions is the sum of candidategeographic locations for all larger geographic region(s) in thegeographic region, and the sum of all candidate geographic location(s)in the smaller geographic region(s) but not in a larger geographicregion(s) in the geographic region.

A number of different ways of determining the number of radios in ageographic region have been illustrate above. The determined number ofradios in a geographic region determined by any means including thoseillustrated above shall be referred to as N′.

In block 108, determine a static dataset, S^(n), and/or a dynamic dataset, M^(l) for every channel in the shared spectra for a geographicregion. The static dataset, S^(n), has n dimensions corresponding to ndata types. Thus, the static dataset comprises a matrix of n*N′*Relements. R is a number of trials using random values of the independentvariables (e.g., Monte Carlo simulation runs). The number of trialsshould be sufficiently large to generate an average statistical behaviorof shared spectrum analytics, e.g., shared spectrum available due tostatic and dynamic incumbent users in channels of the shared spectra.The shared spectrum analytics, e.g., shared spectrum available due tostatic and dynamic incumbent users, is dependent upon by the dependentvariables of the static and dynamic datasets, e.g., derived fromprobabilities of spectrum availability and being on a move list. Byattaining an average statistical behavior of dependent variables of suchdatasets, an average statistical behavior of shared spectrum analyticsis also attained.

The n data types of the static dataset include a radio transmit powerlevel computed to ensure a fair distribution of the interference marginand a first set of radio parameters used to determine the radio transmitpower level. One embodiment of the fair distribution of the interferencemargin is the Iterative Allocation Process (IAP) as described byWInnForum and discussed below. The radio transmit power level is adependent variable determined based upon the first set of radioparameters which are independent variables. Thus, the radio transmitpower level is determined using the first set of radio parameters. Inone embodiment, the values of the first set of radio parameters arerandomly selected for each R runs to determine the radio transmit powerlevel in each set of n*N′ elements of the static data set of thecorresponding run.

In IAP, the radio transmit power level is determined using an iterativeprocess such that the first set of radio parameters ensure that thecumulative interference at a protection point from radios in aneighborhood of a protection point remain below a first threshold powerlevel, e.g., determined by law or regulation. A neighborhood is an area,e.g. a circle, square, or other shape, centered around the correspondingprotection point. However, other techniques may be used. WInnForumShared Access System (SAS) general requirement (requirement) R2-SGN-16of WINNF-TS-0112 defines the IAP, and is hereby incorporated byreference herein in its entirety. The IAP determines such maximumtransmit power levels by allocating interference margin fairly to radiosin neighborhood(s) of protection point(s) proximate to the radios. TheIAP determines such transmit power levels by allocating interferencemargin fairly to radios in neighborhoods of protection point(s), e.g.,of one or more of each of a location of a fixed satellite service, alocation of a priority access license protection area, a grandfatheredwireless protection zone, and location(s) of environmental sensingcapability system(s).

Optionally, after performing IAP, determine if the transmit power levelof any radio in a channel is below a second threshold power level forthe corresponding radio type (for example higher power radios may have ahigher power threshold than lower power thresholds). This embodimentconsiders a practical deployment model where operators may not deployradio if a radio's transmit power is significantly diminished. Byremoving radios with significantly diminished transmit power, thepermitted transmit power of other, remaining radios surroundingprotection point(s) can be more accurately determined. In one example,the second threshold power level is 3 dB below the maximum transmitpower level of the corresponding radio. If any radios have a determinedtransmit power level below a corresponding second threshold power level,then using the randomly selected values of the first set of radioparameters the IAP is performed again for radios determined to have apower level equal to and/or greater than the corresponding secondthreshold power level. This technique eliminates radios from a set ofn*N′ elements of the static dataset corresponding to the unique valuesof the first set of radio parameters used for the IAP. Such radios areeliminated because they are too close to a statically operatingincumbent user (exemplified above), and their removal allows remainingradios in the corresponding set of n*N′ elements to operate at higherpower levels.

The dynamic data set, M^(l), has l dimensions corresponding to l datatypes. Thus, the dynamic dataset comprises a matrix of l*N′*R elements.

The l data types of the dynamic dataset forming each dimension include amove list of radios whose transmission in a shared channel areterminated upon operation of an incumbent system using the sharedchannel. The move list is a dependent variable determined based upon thesecond set of radio parameters which are independent variables. Thus,the move list is determined using the second set of radio parameters. Inone embodiment, the values of the second set of radio parameters arerandomly selected for each R runs to determine the move list in each setof l*N′ elements of the dynamic dataset of the corresponding run.

The move list is a list of radios whose transmission in thecorresponding channel must cease upon an incumbent user utilizing thechannel. Generation of the move list is specified in the WInnForumrequirement identified above. An incumbent user is a dynamic user of achannel, such as a government entity (e.g., naval radar on a ship), thathas preference to use the channel over the radios, e.g., PAL and GAACBSDs. Other data types may be used in addition to or in lieu of one ormore of the data types specified above for the static dataset and/or thedynamic dataset.

The first and second set of radio parameters may or may not be the same.Each set of radio parameters may include parameters such as radiogeographic location, maximum radio transmission power (e.g.,corresponding to radio category), antenna height, antenna azimuth angle,antenna gain, antenna radiation polarization, antenna radiation angle,antenna tilt angle, and/or antenna ground plane data; however, otherradio parameters may be used in addition to or in lieu of theillustrated radio parameters.

The random selection of the values of the first and second set of radioparameters may be accomplished by performing R runs of Monte Carloanalysis. The static and dynamic datasets for each channel in ageographic region are populated by generating a probabilisticdistribution of the different data types, e.g., using Monte Carloanalysis. The radio geographic location is limited to the previouslydetermined candidate geographic locations. Optionally, the number ofradios with higher or highest maximum radio transmission power (e.g.,corresponding to radio category) is constrained by the type ofgeographic morphology of the corresponding geographic region or itsconstituent geographic regions; for example, rural morphologies may havemore higher power radios, subject to maintaining a corresponding minimuminter-radio distance, than an urban morphology of the same area.Optionally, the value of some or all of the radio parameters may beconstrained within a range(s) or set(s) selected by the system user ordesigner.

In block 110, generate at least one set of output data that is astatistical characterization of the radio network, using at least one ofthe static dataset and the dynamic dataset, to determine at least one ofspectrum availability over all geographic regions and likelihood of aradio being placed on a move list over all geographic regions.Optionally, the at least one generated output set may be used to designof at least one radio network using shared spectra. In one embodiment,generating the at least one set of output data comprises generatingoutput data that statistically characterizes parameters of the radionetwork as market penetration increases. In another embodiment, the atleast one data output comprises at least one of:

-   a) a probability of spectrum availability in channels of the shared    spectra in the geographic region;-   b) desirable radio parameters for radios operating in a channel and    at a radio geographic locations;-   c) for a given channel and geographic location, a probability that a    radio at that radio geographic locations and operating on that given    channel will be on a move list;-   d) for a given channel, radio geographic locations requiring a    priority access license due to proximity of other radios;-   e) for a given channel, radio geographic locations where radios    require a backup channel to maintain continuity of service;-   f) for a given channel, analyzing the first set of radio parameters    that give rise to a, e.g., low, probability of spectrum availability    so that a user, e.g., a network operator, can determine if the    probability arises primarily due to a corresponding radio geographic    location's proximity to a static incumbent user or due to another    radio parameter (other than transmit power and radio geographic    location); and-   g) for a given channel, analyzing the second set of radio parameters    that give rise to a, e.g., high, probability of a radio being placed    on a move list so that a user, e.g., a network operator, can    determine if the probability arises primarily due to one or more    radio parameters (other than the corresponding move list).

However, additionally or in lieu of one or more of the foregoing, otheranalyses useful to designing radio networks using shared spectra may begenerated. In one embodiment, the path loss or interference datacollected in the dataset S^(n), for all radio geographic locations, overiterations R, can be used to build a radio map of the frequency band,e.g., the CBRS frequency band of 3550 MHz to 3700 MHz. The radio mapwould identify the maximum possible power transmission at the radiogeographic locations independently without any consideration foraggregation of interference power from all radios in the neighborhood.For example, if a radio at a geographic location causes interferenceproximate to (e.g., within 1-3 dB), a first threshold power level theradio likely will have to reduce radio transmit power level or will beplaced in a move list.

A map of such radio geographic locations susceptible to having transmitpower level reduced or being placed on a move list can be generated andmade available to system users (e.g., engineers who plan radiodeployment, network operators, and/or site survey personnel) forconsideration when they designed radio networks including locations ofradios. The map may also contain information, for such radio geographiclocations, about the impact of incumbent users(s) represented byprotection point(s). The incumbent impact information may be generatedbased upon attributes of the radio, e.g., antenna height, antennaazimuth, radio transmit power, etc. The incumbent impact information maybe stored in a database. The incumbent impact information may begenerated for incumbent user(s) whose operation is either static ordynamic. The incumbent impact information may be a score computed forthe different radio geographic locations and saved in the database. Theincumbent impact information or score may represent a level of impact bya radio at radio geographic location on the incumbent user(s). Forexample, a higher score implies that a radio at the radio geographiclocation is more highly influenced by the incumbent user(s), and thusmore likely to have its transmit power reduced or to be placed on a movelist. The incumbent impact information or score may be represented onthe map (and may displayed to a user) using different colors, e.g.,transitioning from no color to shades purple to shades of red asillustrated below corresponding to regions of lower to higher impact.The mapping techniques described herein are improvements to atechnological system for modeling deployment of radios because they moreefficiently identify to a user of the modelling system what preferentialaction should be taken, e.g., where radios are preferably deployed onthe map or where a PAL license should be purchased. Other color schemescan be used for the mapping described above and elsewhere herein.Further, other indicia, e.g., textual symbols can be used to distinguishdifferent regions on a map, e.g., desirable and undesirable regions on amap. In lieu of a map, data representative of mapped information may begenerated in tabular form using numerical or alpha-numericrepresentations for location and corresponding values, and may be storedin one or more data files and/or databases. Such tabular form data maybe used by other design tools.

One embodiment of the use of the radio map is to provide guidance inselecting radio deployment sites. The system may utilize an interface,e.g. a user could identify a desired deployment location through theinterface and obtain through the interface a score for the location. Theinterface could also provide the conditions (antenna height, antennaazimuth, radio transmit power, etc.) under which a radio deployed at thelocation would not significantly impact static or dynamic incumbentuser(s). When the system user provides additional installationparameters along with the desired location, the interface can be used toprovide a better design criterion or a tailored result. One examplewould be the antenna azimuth information for the desired location couldbe provided by the user to the interface. The interface would thenprovide the likely antenna heights at which the radios could be deployedto minimize the impact to the incumbent user(s).

In the case where the desired location is not in the database, onemethod would be to use the closest location for extrapolating theresults. Other techniques like averaging, interpolating between the datapoints can also be used. One technique to measure the radio's impact toa static incumbent user(s) is the extent of reduction to the desiredtransmit power of the radio. The more there is a reduction in allowabletransmit power, the greater is the impact of the radio on the incumbentuser(s). In the dynamic incumbent user case, the probability of theradio being on the move list would be the measure for evaluating theimpact of the radio on the dynamic incumbent user.

In one embodiment, spectrum availability in the shared spectra in thegeographical region may be determined by calculating a spectrumavailability probability, PSA, for the geographic region (e.g., on alarger geographic region and/or a smaller geographic region basis) andchannel. For example, spectrum availability probability for eachgeographic location of radios for a given channel is:

PSA=|{xεS ¹ |x>Th}|/(N′*R),  (Equation 4)

where |{xεS^(l)|x>Th}| means a cardinal number of radios created duringthe R runs determined to have a transmit power level above a thirdthreshold power level Th, and for a corresponding, e.g., 10 MHz,channel. The channel bandwidths illustrated herein are exemplary and canbe smaller or larger. The third threshold power level, for example, maybe 3 dB below maximum power level of the corresponding radio. PSA=0 ifthe corresponding radio is located in an exclusion zone where radios areprohibited. Exclusion zones are comprised, e.g., of protection point(s),and may result from any of the systems or regions described above withrespect to protection points.

The spectrum availability probability for a channel can be overlaid upona map of the geographic region to visually illustrate the probability.For example, the following color mapping can be used to visuallyillustrate the spectrum availability probability:

Spectrum Availability Probability Range Description Color P_(SA) = 0   Not Available Purple   0 < P_(SA) < 10% Least Available Red 10% ≤ P_(SA)< 50% Less Likely Available Orange 50% ≤ P_(SA) < 90% Likely AvailableYellow  90% ≤ P_(SA) < 100% Most Likely Available Green P_(SA) = 100%Available no colorThe colored map may be displayed to a user, and is an improvement to asystem for modeling deployment of radios because it more efficientlyidentifies where radios are preferably deployed on the map, e.g., wherethe availability of spectrum is high.

However, other schemes, e.g., other spectrum availability probabilityranges and color combinations, can be used to visually illustrate theprobability for the channel on a map of the geographic region.Geographic locations of radios in the geographic region (for a specificchannel) having a relatively high spectrum availability probability,e.g., greater than 50% or 90%, may be considered by a radio networkoperator as desirable geographic locations for additional radios to beinstalled in the near term or future.

In one embodiment, the probability that a radio at a geographic locationwill be on a move list for a given channel (where a dynamic incumbentsystem may operate) may be determined by calculating a move listprobability, PM, for the geographic region and channel of an incumbentsystem whose operation causes transmissions of radios—on theirauthorized channels in the shared spectra—to cease. For example, if theincumbent system is naval radar on a ship, the incumbent system'schannel is 10 MHz. For example, move list probability for eachgeographic location of a radio and channel is:

PM=|M ^(l)|/(N′*R),  (Equation 5)

where |M^(l)| means a cardinal number of radios of all move listscreated during the R runs and for a corresponding, e.g., 10 MHz,channel.

The move list probability for a channel can be overlaid upon a map ofthe geographic region to visually illustrate the probability. Forexample, the following color mapping can be used to visually illustratethe move list probability:

Move List Probability Range Description Color Code Pm = 0% Not likely inmove list no color  0% < Pm < 10% Least Likely Green 10% ≤ Pm < 50% LessLikely Yellow 50% ≤ Pm < 90% Likely Orange  90% ≤ Pm < 100% HighlyLikely RedThe colored map may be displayed to a user, and is an improvement to asystem for modeling deployment of radios because it more efficientlyidentifies where radios are preferably deployed on the map, e.g., wherethe likelihood that a radio will be placed on a move list is low.

However, other schemes, e.g., other move list probability ranges andcolor combinations, can be used to visually illustrate the move listprobability for the channel on a map of the geographic region.Geographic locations of radios in the geographic region (for a specificchannel) having a relatively high move list probability, e.g., greaterthan 50 or 90%, may be considered by a radio network operator asundesirable geographic locations for additional radios to be installedin the near term or future. In one embodiment, desirable geographicallocations and/or installation parameters for radios operating in afrequency channel and located in the geographical regions can beimplemented by identifying radio parameters corresponding to set ofR*n*N′ elements and set of R*l*N′ elements of both the static anddynamic datasets that result in relatively high spectrum availabilityprobability, e.g., greater than 50% or 90%, and a relatively low movelist probability, e.g., less than 50% or 10%. Although certainprobability ranges are illustrated herein, other ranges may be used.

In one embodiment, for a given channel, analyze the second set of radioparameters that give rise to, e.g., a high probability, of a radio beingplaced on a move list so that a user,

e.g., a network operator, can determine if the probability arisesprimarily due to one or more radio parameters (other than thecorresponding move list). For example, such analysis can determine acorrelation between one or more radio parameters, e.g., location andmaximum radio transmit power, and an increased probability that a radiolocation is placed on the move list. The correlations can be displayedin a map or in tabular format in the manners described elsewhere hereinfor displaying other types of data. Such correlations can be used to aidin planning future radio deployments, such as changing parameters ofradios whose deployment is being planned, in a geographic region, and todetermine whether the network operator needs to depend upon anotherspectrum, e.g., a licensed spectrum, for continuity of service.

For a potential radio site in an area having a relatively low spectrumavailability probability across a frequency band (e.g., the CBRSfrequency band), e.g., less than 50% or 10%—for each specific channel,the method may suggest that a network operator should considerprocuring, e.g., purchasing, a priority access license (PAL) for a radioat a geographic location to ensure access to the specific channel at thegeographic location. The need for a priority access license may arise ifthere is a high density of radios, e.g., GAA CBSDs, in that area in ageographic region. The method for evaluating the recommendation forprocuring a PAL in a geographic region would evaluate the impact of oneor more radios on the incumbents individually and in an aggregate sense.The individual impact evaluation checks if the radio has an interferencepower at the incumbent that exceeds a first threshold, e.g., −109dBm/MHz in the case of ESC incumbent. The aggregate impact analysisperforms a new spectrum availability probability analysis, as defined inequation 4, by using a PAL radio deployment, as described below. A score(PAL Impairment County Score is generated to indicate the level ofimpairment on the PAL channels for a larger geographic region, e.g., acounty, where a PAL is desired, as described in the subsequent section.The impairment is a measure of the impact on PAL radios due toprotection requirement for the static or dynamic incumbents, for examplethe operational power of radios with PAL license may be restricted to avalue below a threshold power, e.g., 30 dBm/MHz for category B and 20dBm/MHz for category A, due to static incumbents, or that the radio withPAL license may have a probability of being placed on move-list above aprobability threshold, e.g., a probability of 0.60 due to dynamicincumbent. The PAL impairment score can a continuous range of numbersfrom lowest, e.g., 0, which indicated least amount of impairment, to thehighest value, e.g., 10, which indicates a substantial impairment on PALradios; alternative numerical ranges can be used. Optionally, PALimpairment score is assigned per county when PALs are issued for anentire county area. For pedagogical purposes, this score per countyembodiment will be subsequently exemplified; however, the score may bebased upon any region to which a PAL is assigned. The PAL ImpairmentCounty Score (PICS) consists of 2 underlying scores: a county-static PALchannel impairment score (C-SPCIS) and a county-dynamic PAL channelimpairment score (C-DPCIS). A method, described below, provides the userwith the PICS, C-SPCIS and the C-DPCIS scores for a county.

The county-static PAL channel impairment (CSPCI) score represents thePAL channel impairment for a county based on the static incumbentanalysis. A high PAL impairment score, e.g. 10, means radios in thegeographic region operating on the PAL channels will not be able totransmit above or at the threshold operational power, e.g., 30 dBm/MHzfor category B and 20 dBm/MHz for category A, while a low PAL impairmentscore, e.g., 0, means radios in the geographic region operating on thePAL channels will be able to transmit at least at threshold operationalpower.

The county-dynamic PAL channel impairment (CDPCI) score represents thePAL channel impairment score for a county based on the dynamic incumbentanalysis. PAL impairment in this context means a high probability ofbeing on the move-list for radios transmitting on the PAL channel. Ahigh PAL impairment score means radios in the geographic regionoperating on the PAL channel have a high probability of being on theMove List (ML), while a low PAL impairment score means radios in thegeographic region operating on the PAL channel have a lower probabilityto be on the ML.

Estimating County Static PAL Channel Impairment Score (CSPCI)

For a given county of interest, an estimated CSPCI score can bedetermined as the sum of the subcounty static PAL channel impairment,SSPCI, scores of the sub counties within a county of interest. For eachsubcounty, the first step is to assess the spectrum availabilityprobability, PSA exemplified in equation 4, using the static dataset,S^(n), for each PAL channel, where a binary classification is made basedon the spectrum availability probability, i.e. high or low spectrumavailability. For example, if for a PAL channel in a sub-county theprobability of spectrum availability is above a threshold, example 0.60,then the spectrum availability for the PAL channel is considered high,otherwise it is considered a low spectrum availability PAL channel.

PAL channels are divided into two groups, ones having high spectrumavailability and the remaining PAL channels with low spectrumavailability, where low and high spectrum availability have beendescribed elsewhere herein. For the subset of PAL channels having highspectrum availability, a PAL channel impairment score (PCIS) is assigneda low value, e.g. the value 0. For the subset of PAL channels with lowspectrum availability, a PCIS score will be determined based on whetherthe radio's low spectrum availability is caused by aggregation-type orsingle-exposure-type interference (to incumbents). Asingle-exposure-type classification means that the low spectrumavailability is caused by the radio parameters of each radio, i.e.,without aggregation, or even when there is low density radio deployment,while an aggregation-type classification means that the low spectrumavailability is due to the aggregation of interference from all of theradios in the subcounty, i.e., due to high density radio deployment.Optionally, the interference classification may be obtained from afunction of the radio and interference parameters of the set of deployedradios within the subcounty. The function will return a value 1 toindicate an aggregation-type power adjustment cause, or a 0 to indicatesingle-exposure-type power adjustment cause. The following is adescription of such an embodiment.

For the set C of all radios deployed within the smaller region, e.g.,subcounty, determine a first subset of radios within the set C, C′,which consists of the subset of radios with adjusted power that is inexcess a Power Adjustment Threshold, e.g. 7 dB. From the subset ofradios determine the subset of radios C″ that have an interference powerto their associated minimum protection point that exceeds anInterference to Minimum Protection Point Threshold, e.g., −90 dBm/MHz.If the ratio |C″|/|C| is greater than Single Exposure Ratio Threshold,e.g., 0.5, then return 1, otherwise it returns a 0. Therefore, areturned value 1 corresponds to low spectrum availability due to theradio parameters of each radio, i.e., not due to high-density radiodeployment, while a returned value of 0 indicates a low spectrumavailability due to the aggregation of interference due to ofhigh-density radio deployment in the set C. The function described aboveis herein referred to as F. The mathematical operator | | in the ratiocorresponds to the cardinality value, or number of elements, of theenclosed set.

When the output of the function F is 1, i.e., indicatingaggregation-type, a second instance of probability of spectrumavailability, P′SA_m, evaluation is conducted for mth PAL channel, e.g.,for channels 1 to 10, where PAL radio deployment will be modeled in aconstituent region, e.g., the subcounty. Since the presence of PALradios in a subcounty will result in reduction of the deployment densityof radios, specifically GAA radios (due to removal of GAA radios fromPAL Protection Areas (PPAs)), the aggregate interference to impactedincumbents in the subcounty will be changed, and thus the spectrumavailability due to PAL presence may correspondingly change. A metric ofthe mth PAL channel, PAL_ChanImpairment_(PALsubCounty) _(m) , where m isone of the PAL channels, in the subcounty, is defined as a function ofprobability of spectrum availability in the mth PAL channel in thesubcounty, P′SA_m. The PAL_ChanImpairment_(PALsubCounty) _(m) metric maybe assigned a binary value of 0 when second probability of spectrumavailability (P′SA_m) of the mth PAL channel is high, or a value 1 whenthe second probability of spectrum availability of the mth PAL channelis low. To summarize in equation form, the above described PAL channelimpairment function for mth PAL channel in a subcounty is:

$\begin{matrix}{{PAL\_ ChanImpairment}_{{PALsubCounty}_{m}} = \left\{ \begin{matrix}{0,{{when}\mspace{14mu} {second}\mspace{14mu} {spectrum}\mspace{14mu} {availablilty}},} \\{{P^{\prime}{SA\_ m}},{{for}\mspace{14mu} {mth}\mspace{14mu} {chan}\mspace{14mu} {PAL}\mspace{14mu} {is}\mspace{14mu} {high}}} \\{1,{{when}\mspace{14mu} {second}\mspace{14mu} {spectrum}\mspace{14mu} {availablilty}},} \\{{P^{\prime}{SA\_ m}},{{for}\mspace{14mu} {mth}\mspace{14mu} {chan}\mspace{14mu} {PAL}\mspace{14mu} {is}\mspace{14mu} {low}}}\end{matrix} \right.} & \left( {{Equation}\mspace{14mu} 4} \right)\end{matrix}$

Alternatively, the mth PAL channel metric,PAL_ChanImpairment_(PALsubCounty) _(m) , may map to a continuous value,e.g., 0.01 to 0.99, as a function of the second instance of probabilityof spectrum availability, P′SA_m, of the mth PAL channel as follows:

PAL_ChanImpairment_(PALsubCounty) _(m) =1−P′SA_m(for mth PAL chan)  (Equation 7)

To determine the PAL channel impairment score for a subcounty for allPAL channels, the sum of PAL_ChanImpairment_(PALsubCounty) _(m) valuesfor all M PAL channels is computed, which is expressed in equation formas:

$\begin{matrix}{{{Pal}\mspace{14mu} {Channel}\mspace{14mu} {Impairment}\mspace{14mu} {for}\mspace{14mu} {Subcounty}\mspace{14mu} ({PCIS})} = {\sum\limits_{m}^{M\mspace{14mu} {PAL}\mspace{14mu} {channels}}{PAL\_ ChanImpairment}_{{PALsubCountyChan}_{m}}}} & \left( {{Equation}\mspace{14mu} 8} \right)\end{matrix}$

For example, if for a subcounty it is determined that availablebandwidth is determined as SA_(PALsubCountyChan) _(m) =1,1,1,1,1,1,0,0for m=1 to 8, respectively, then PCIS=6 for this subcounty. In anotherexample, for another subcounty SA_(PALsubCountyChan) _(m)=0,0,0,0,0,0,0,0 for m=1 to 8, respectively, then PCIS=0.

The County Static PAL Channel Impairment (CSPCI) score for the countycan be determined as a function of the Subcounty Static PAL ChannelImpairment, SSPCI, scores in the county. In one embodiment the functionmay be the weighted sum of the individual subcounty SSPCI_(n), where nis the index of nth subcounty, score, as follows:

CSPCI=Σ^(numSubCountyInCounty) _(N) w _(n)*SSPCI_(n)   (Equation 9)

where each weight w_(n) is determined as a function of the subcountypopulation ratio to the total county population, i.e.:

$\begin{matrix}{{w_{n} = \frac{\left( {{population}\mspace{14mu} {of}\mspace{14mu} {nth}\mspace{14mu} {subcounty}} \right)}{{total}\mspace{14mu} {County}\mspace{14mu} {Population}}},} & \left( {{Equation}\mspace{14mu} 10} \right)\end{matrix}$

where a subcounty may be a geographic region of a set of adjacent censustracts, or a region that may include entire or partial census tracts.Alternatively, the weight can be determined from the ratio of the numberof radios deployed in a subcounty to the total number of radios in thecounty in which the subcounty is located, i.e.:

$\begin{matrix}{w_{n} = \frac{\left( {{number}\mspace{14mu} {of}\mspace{14mu} {CBS}\; {Ds}\mspace{14mu} {deployed}\mspace{14mu} {in}\mspace{14mu} {nth}\mspace{14mu} {subcounty}} \right)}{{total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {CBSD}\mspace{14mu} {in}\mspace{14mu} {county}}} & \left( {{Equation}\mspace{14mu} 11} \right)\end{matrix}$

For example, if a county has a high computed SCPCI score, e.g., 10, thena PAL will be highly likely to be impaired within this county. On theother hand, if a county has a low SCPCI score, e.g., 0, then a PAL willbe highly likely to be not impaired in this county.

Estimating County Dynamic PAL Channel Impairment (CDPCI) Score

For a given county of interest, an estimated CDPCI score can bedetermined as the sum, or weighted sum, of the subcounty dynamic PALchannel impairment, SDPCI, scores of the sub-counties in a county ofinterest. For each subcounty, the first step is to assess the move listprobability using the dynamic dataset M^(l). Unlike Static PALimpairment analysis, when computing move-list probability there is nodistinction between PAL channels since the impact on the incumbent isequal across all PAL channels, therefore, the estimated move-listprobability applies equally to all PAL channels.

The initial estimate of move-list probability for the geographic region,or sub-county, is compared to a probability threshold, e.g., 0.6, and ifthe probability is below the threshold, then the Dynamic PAL impairmentis assigned a low value, e.g., 0. If, on the other hand, the estimatedmove-list probability is above the probability threshold, high move listprobability, then the next step is to determine whether the cause ofhigh move-list probability is due to aggregation of interference fromhigh density radios in the subcounty or if it is due interference levelsfrom individual radios above the DPA's protection threshold, e.g., −144dB/MHz, at the associated DPA's protection points. The formerinterference case is referred to as aggregation-type, while the latteris referred to as the single-exposure-type. The cause of high move-listprobability may be determined as by the following steps:

-   -   For a set C of all radios located in the subcounty, determine a        subset of radios C′, which consists of the radios having        interference power at any associated protection point in the        neighboring DPA that is greater than a protection threshold,        e.g., −144 dBm/MHz;    -   Next, determine a ratio |C′|/|C|, where |C′| and |C| represents        the cardinal value of the set C′ and the set C, respectively;        and    -   If the determined ratio |C′|/|C| is greater than a ratio        threshold, e.g., 0.5, then the high move-list probability is        classified as the single-exposure-type case, otherwise the cause        is classified as the aggregation-type case.

If single exposure interference is determined as the cause of highmove-list probability, then the PAL impairment score will be set to ahigh value, e.g., 10, otherwise, a second instance of move-listprobability estimation is conducted, where PAL radios are located in thesubcounty. The second instance move list probability is determined in amanner similar to the move list determination as described earlier withPAL radios being present in the subcounty. Since the presence of PALradios in a subcounty will result in reduction of the deployment densityof radios, specifically GAA radios (due to removal of GAA radios fromPAL Protection Areas (PPAs)), the aggregate interference to impactedincumbents in the subcounty will be changed, and therefore the move listprobability due to PAL radios' presence may correspondingly change. Ifthe estimated second instance move-list probability is above athreshold, e.g., 0.6, then the PAL impairment score will be set to ahigh value, e.g., 10, otherwise the score will be set to a low value,e.g., 0. Alternatively, the PAL impairment score may be determined as acontinuous range from lower end to the higher end as a function of thesecond instance move-list probability estimate, specifically it may bedirectly proportional to the second instance move-list probability suchas PAL impairment score=(10.0*the second instance move-list probabilityestimate). For example, when second instance move list probability is0.5, the PAL impairment score=10.0*0.5=5.

The CDPCI score for the county can be determined as a function of thedynamic subcounty PAL channel impairment scores in the county. In oneembodiment the function may be the weighted sum of the individualsubcounty, SDPCI_(n), where n is the index of the nth subcounty, score,as follows:

$\begin{matrix}{{CDPDI}{= {\sum\limits_{n}^{numSubCountyInCounty}{w_{n}*{SDPCI}_{n}}}}} & \left( {{Equation}\mspace{14mu} 12} \right)\end{matrix}$

In one embodiment the function may be the weighted sum of the individualsubcounty scores where each weight is determined as a function of thesubcounty population ratio to the total county population, i.e.:

$\begin{matrix}{{w_{n} = \frac{\left( {{population}\mspace{14mu} {of}\mspace{14mu} {nth}\mspace{14mu} {subcounty}} \right)}{{total}\mspace{14mu} {County}\mspace{14mu} {Population}}},} & \left( {{Equation}\mspace{14mu} 13} \right)\end{matrix}$

where a subcounty may be a geographic region of a set of adjacent censustracts, or a region that may include entire or partial census tracts.Alternatively, the weight can be determined from the ratio of the numberof radios deployed in a subcounty to the total radios in the county,i.e.:

$\begin{matrix}{w_{n} = \frac{\left( {{number}\mspace{14mu} {of}\mspace{14mu} {CBS}\; {Ds}\mspace{14mu} {deployed}\mspace{14mu} {in}\mspace{14mu} {nth}\mspace{14mu} {subcounty}} \right)}{{total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {CBSD}\mspace{14mu} {in}\mspace{14mu} {county}}} & \left( {{Equation}\mspace{14mu} 14} \right)\end{matrix}$

For example, if a county has a computed high CDPCI score, e.g., 10, thena PAL will be highly likely to be impaired within this county. On theother hand, if a county has a low CDPCI score, e.g., 0, then the PALwill highly likely not be impaired in this county.

Optionally, the operator can use the static analysis and dynamicanalysis-based PAL channel impairment scores, CSPCI and CDPCI,separately to help decide whether he wants to bid for the PAL for aparticular county. Optionally, the operator can choose to retrieve acombined overall PAL impairment score for the county of interest that isa weighted sum of the static and dynamic analysis metrics. The staticanalysis metric can be weighted more than the dynamic analysis metricsince the impact of the static incumbents is always present while theDPA activation is sporadic. The colored map may be displayed to a userof the combined overall PAL impairment score, the static PAL impairmentscore, and/or the dynamic PAL impairment score, and is an improvement toa system for modeling deployment of radios because it more efficientlyidentifies where it is more beneficial to purchase a PAL for a radio,e.g., where PAL impairment will be low.

FIG. 2 illustrates one embodiment of a shared spectra radio networkplanning. system 200. The illustrated shared spectra radio networkplanning system 200 comprises a processing circuitry 222. The processingcircuitry may be a state machine, a neural network, and/or any othertype of processing circuitry; for pedagogical reasons, processingcircuitry is illustrated herein one form of a state machine. Optionally,the processing circuitry 222A is communicatively coupled to at least oneinput/output device (I/O(s)) 226. Optionally, the processing circuitry222A is coupled to at least one communications system (communicationssystem(s)) 224. For pedagogical purposes, the shared spectra radionetwork planning system 200 will be illustrated with such externalcomponents not being part of the radio network planning system 200.

The processing circuitry 222 principally performs a planning functionfor a network of one or more radios that utilize shared spectra. Thecommunications system 224 facilitates communications between theprocessing circuitry 222 and external components, e.g., at least oneexternal database (external database(s) 228A and/or at least oneexternal analysis system (external analysis system(s)) 228B. Suchexternal components will be subsequently discussed. The communicationssystem 224 comprises communications circuitry such as Internet modemcircuitry and/or radio circuitry.

Additionally or alternatively, the communications systems 224 can beused to communicatively couple remote user(s) to the shared spectraradio network planning system 200; in such an event, the shared spectraradio network planning system 200 may not require the I/O(s) 226. Suchremote user(s) are located at a distance from the shared spectra radionetwork planning system 200, and may be person(s) and/or computersystem(s).

The I/O(s) 226 are man machine interfaces that a user, such as a person,to interact with the shared spectra radio network planning system 200.The I/O(s) 226 may comprise a keyboard, a mouse, a joystick, amicrophone and/or a voice recognition system, a touch screen display, anon-touch screen display (e.g., an LCD or OLED display), and/or aspeaker and/or a voice synthesizer.

In the illustrated embodiment, the processing circuitry 222 includesprocessor circuitry 222A coupled to memory circuitry 222B. In theillustrated embodiment, the memory circuitry 222A includes at least onedatabase (database(s)) 222A-1 and at least one analysis system (analysissystem(s)) 222A-2. The analysis system(s) 222A-2 are executed by theprocessor circuitry 222B to model a communications network that utilizesshared spectra, e.g., utilizing the method illustrated above. However,some of the modelling, for example, determination of radio transmitpower level and/or move lists can be performed by external analysissystem(s) 228B, e.g., by IAP analysis system(s) in shared accesssystem(s); for example, such shared access system(s) may already controlradios utilizing the shared spectra and located in the geographic regionbeing modelled.

The analysis system(s) 222A-2 and/or the external analysis system(s) mayinclude propagation modelling system(s) which estimate electromagneticenergy radiated by a radio's antenna at geographic location(s) and/orover geographic region(s). The propagation modelling system(s) mayinclude one or more types of RF propagation models, which describe pathloss, over geographic region, of signals emitted by radio(s) underdifferent propagation conditions. The selection of a propagation modeldepends upon frequency spectrum, the propagation path (e.g., includingdistance, geographical terrain, morphology, and physical obstructionssuch as buildings), antenna characteristics (e.g., angle of radiationand radiation polarization), potential atmospheric conditions (e.g.,ionospheric conditions and the existence of meteor showers), and/or time(such as time of day of operation of the radio(s) and/or solar cycledata). The propagation models may be public and/or proprietary models.Examples of propagation models include the Hata model, the Longley-Ricemodel, and variations thereof.

The database(s) 222A-1 store data used to perform the modelling and/ordata generated by the modelling. However, optionally, some of such datamay be stored, at least in part, in external database(s) 228A, such aspopulation data for geographic regions stored in database(s) at the U.S.Census Bureau, geographic morphology data stored in database(s) at theU.S. Geological Survey, map data stored in database(s) at map dataproviders such as Google, and/or data about existing radio networks,incumbent user(s)/system(s), and priority access licensee radios in ornear a geographic region and using the shared spectra being modelledwhich may be stored in database(s) at the U.S. Federal CommunicationsCommission, National Telecommunications and Information Administration,and/or shared access system(s); for example, such shared accesssystem(s) may already control radios utilizing the shared spectra andlocated in the geographic region being modelled. Some data in thedatabase(s) 222A-1 may be entered by the user or may be implemented bythe system as described elsewhere herein. Data used to perform themodelling, includes the population data for geographic regions,

The processor circuitry described herein may include one or moremicroprocessors, microcontrollers, digital signal processing (DSP)elements, application-specific integrated circuits (ASICs), and/or fieldprogrammable gate arrays (FPGAs). In this exemplary embodiment,processor circuitry includes or functions with software programs,firmware, or other computer readable instructions for carrying outvarious process tasks, calculations, and control functions, used in themethods described herein. These instructions are typically tangiblyembodied on any storage media (or computer readable medium) used forstorage of computer readable instructions or data structures.

The memory circuitry described herein can be implemented with anyavailable storage media (or computer readable medium) that can beaccessed by a general purpose or special purpose computer or processor,or any programmable logic device. Suitable computer readable medium mayinclude storage or memory media such as semiconductor, magnetic, and/oroptical media. For example, computer readable media may includeconventional hard disks, Compact Disk—Read Only Memory (CD-ROM), DVDs,volatile or non-volatile media such as Random Access Memory (RAM)(including, but not limited to, Dynamic Random Access Memory (DRAM)),Read Only Memory (ROM), Electrically Erasable Programmable ROM (EEPROM),and/or flash memory. Combinations of the above are also included withinthe scope of computer readable media.

Methods of the invention can be implemented in computer readableinstructions, such as program modules or applications, which may bestored in the computer readable medium and executed by the processorcircuitry. Generally, program modules or applications include routines,programs, objects, data components, data structures, algorithms, and thelike, which perform particular tasks or implement particular abstractdata types.

Databases as used herein may be either conventional databases or datastorage formats of any type, e.g., data files. Although separatedatabases are recited herein, one or more of such databases may becombined.

Exemplary Embodiments

Example 1 includes a method of modelling a radio network in a geographicregion utilizing shared spectra, comprising: obtaining population datafor the geographic region; determining a number of radios per channel inthe geographic region, N′; determining candidate geographic location forradios in the geographic region; determining, for every channel in theshared spectra, at least one of a static dataset and a dynamic dataset;wherein the static dataset comprises a set of n*N′*R elements, where nis a number of data types, comprising at least one independent variableand at least one dependent variable, of the static dataset and R is anumber of trials to determine the at least one dependent variable usingrandom values of the at least one independent variable, where the datatypes comprise a first set of radio parameters whose values are randomlyselected and a radio transmit power level for a channel, and where theradio transmit power level for the channel is determined so as todistribute interference margin amongst radios in a neighborhood of aprotection point or an incumbent user using the randomly selected valuesof the first set of radio parameters; wherein the dynamic datasetcomprises a set of l*N′*R elements, where l is a number of data types,comprising at least one independent variable and at least one dependentvariable, of the dynamic data set, where the data types comprise asecond set of radio parameters whose values are randomly selected and amove list of radios, operating in the channel, whose transmissions areterminated upon operation of an incumbent system using the channel, andwhere the move list is determined with the randomly selected values ofthe second set of radio parameters; and generating at least one set ofoutput data that is a statistical characterization of the radio network,using at least one of the static dataset and the dynamic dataset, todetermine at least one of spectrum availability over all geographicregions and likelihood of a radio being placed on a move list over allgeographic regions.

Example 2 includes the method of Example 1, wherein the determined atleast one of spectrum availability over all geographic regions and thedetermined likelihood of a radio being placed on a move list over allgeographic regions are displayed as a map of geographic region and usingdifferent indicia to indicate a range of the corresponding availabilityand a range of the corresponding likelihood.

Example 3 includes the method of any of Examples 1-2, further comprisingdetermining at least one of combined overall PAL impairment, static PALimpairment, and dynamic PAL impairment based upon at least one of thedetermined at least one spectrum availability and the determinedlikelihood of a radio being placed on a move list.

Example 4 includes the method of Example 3, wherein the at least one ofcombined overall PAL impairment, static PAL impairment, and dynamic PALimpairment are displayed as a map of a geographic region and usingdifferent indicia to indicate a range of the corresponding impairment.

Example 5 includes the method of any of Examples 1-4, wherein generatingthe at least one set of output data comprises generating output datathat statistically characterizes parameters of the radio network asmarket penetration increases.

Example 6 includes the method of any of Examples 1-5, wherein the numberof candidate geographic locations for a geographic region that is asmaller geographic region is determined by multiplying the number ofradios per channel in the geographic region by a scaling factor.

Example 7 includes the method of Example 6, wherein the scaling factoris based upon a user defined minimum inter-radio distance.

Example 8 includes the method of any of Examples 6-7, wherein a productof the multiplication is either rounded up or a floor mathematicalfunction is applied to the product.

Example 9 includes the method of any of Examples 1-8, whereindetermining the static data set further comprises: determining if thetransmit power level of any radio in the neighborhood and in the channelis below a threshold power level; and upon determining that the transmitpower level of one or more radios in the neighborhood and in the channelare below the threshold, then determine a radio transmit power level,for a channel and for radios in the neighborhood determined to have atransmit power equal to or greater than the threshold power level, thatdistributes interference margin amongst the determined radios using therandomly selected values of the first set of radio parameters.

Example 10 includes the method of any of Examples 1-9, furthercomprising determining a maximum possible transmission power at eachcandidate radio location in the absence of interference from otherradios; and displaying a map of a candidate radio locations in ageographic region using different indicia to indicate a range of thecorresponding maximum possible transmission power at each candidateradio location.

Example 11 includes a system, comprising: processing circuitryconfigured to: obtain population data for the geographic region;determine a number of radios per channel in the geographic region, N′;determining candidate geographic location for radios in the geographicregion; determining, for every channel in the shared spectra, at leastone of a static dataset and a dynamic dataset; wherein the staticdataset comprises a set of n*N′*R elements, where n is a number of datatypes, comprising at least one independent variable and at least onedependent variable, of the static dataset and R is a number of trials todetermine the at least one dependent variable using random values of theat least one independent variable, where the data types comprise a firstset of radio parameters whose values are randomly selected and a radiotransmit power level for a channel, and where the radio transmit powerlevel for the channel is determined so as to distribute interferencemargin amongst radios in a neighborhood of a protection point or anincumbent user using the randomly selected values of the first set ofradio parameters; wherein the dynamic dataset comprises a set of l*N′*Relements, where l is a number of data types, comprising at least oneindependent variable and at least one dependent variable, of the dynamicdata set, where the data types comprise a second set of radio parameterswhose values are randomly selected and a move list of radios, operatingin the channel, whose transmissions are terminated upon operation of anincumbent system using the channel, and where the move list isdetermined with the randomly selected values of the second set of radioparameters; and generate at least one set of output data that is astatistical characterization of the radio network, using at least one ofthe static dataset and the dynamic dataset, to determine at least one ofspectrum availability over all geographic regions and likelihood of aradio being placed on a move list over all geographic regions.

Example 12 includes the system of Example 11, wherein the determined atleast one of spectrum availability over all geographic regions and thedetermined likelihood of a radio being placed on a move list over allgeographic regions are displayed as a map of geographic region and usingdifferent indicia to indicate a range of the corresponding availabilityand a range of the corresponding likelihood.

Example 13 includes the system of any of Examples 11-12, furthercomprising determine at least one of combined overall PAL impairment,static PAL impairment, and dynamic PAL impairment based upon at leastone of the determined at least one spectrum availability and thedetermined likelihood of a radio being placed on a move list.

Example 14 includes the system of any of Examples 11-13, wherein the atleast one of combined overall PAL impairment, static PAL impairment, anddynamic PAL impairment are displayed as a map of a geographic region andusing different indicia to indicate a range of the correspondingimpairment.

Example 15 includes the system of Examples 11-14, wherein generating theat least one set of output data comprises generating output data thatstatistically characterizes parameters of the radio network as marketpenetration increases.

Example 16 includes the system of any of Examples 11-15, wherein thenumber of candidate geographic locations for a geographic region that isa smaller geographic region is determined by multiplying the number ofradios per channel in the geographic region by a scaling factor.

Example 17 includes the system of Example 16, wherein the scaling factoris based upon a user defined minimum inter-radio distance.

Example 18 includes the system of any of Examples 16-17, wherein aproduct of the multiplication is either rounded up or a floormathematical function is applied to the product.

Example 19 includes the system of any of Examples 11-18, whereindetermining the static data set further comprises: determining if thetransmit power level of any radio in the neighborhood and in the channelis below a threshold power level; and upon determining that the transmitpower level of one or more radios in the neighborhood and in the channelare below the threshold, then determine a radio transmit power level,for a channel and for radios in the neighborhood determined to have atransmit power equal to or greater than the threshold power level, thatdistributes interference margin amongst the determined radios using therandomly selected values of the first set of radio parameters.

Example 20 includes the method of any of Examples 11-19, wherein theprocessing circuitry is further configured to determine a maximumpossible transmission power at each candidate radio location in theabsence of interference from other radios; and display a map of acandidate radio locations in a geographic region using different indiciato indicate a range of the corresponding maximum possible transmissionpower at each candidate radio location.

Example 21 includes the system of any of Examples 11-20, wherein theprocessing circuitry is further configured to determine a correlationbetween at least one radio parameter and probability of being placed ona move list, where the at least one radio parameter comprises radiolocation.

Example 22 includes a program product comprising a non-transitoryprocessor readable medium on which program instructions are embodied,wherein the program instructions are configured, when executed by atleast one programmable processor, to cause the at least one programmableprocessor to: obtain population data for the geographic region;determine a number of radios per channel in the geographic region, N′;determining candidate geographic location for radios in the geographicregion; determining, for every channel in the shared spectra, at leastone of a static dataset and a dynamic dataset; wherein the staticdataset comprises a set of n*N′*R elements, where n is a number of datatypes, comprising at least one independent variable and at least onedependent variable, of the static dataset and R is a number of trials todetermine the at least one dependent variable using random values of theat least one independent variable, where the data types comprise a firstset of radio parameters whose values are randomly selected and a radiotransmit power level for a channel, and where the radio transmit powerlevel for the channel is determined so as to distribute interferencemargin amongst radios in a neighborhood of a protection point or anincumbent user using the randomly selected values of the first set ofradio parameters; wherein the dynamic dataset comprises a set of l*N′*Relements, where l is a number of data types, comprising at least oneindependent variable and at least one dependent variable, of the dynamicdata set, where the data types comprise a second set of radio parameterswhose values are randomly selected and a move list of radios, operatingin the channel, whose transmissions are terminated upon operation of anincumbent system using the channel, and where the move list isdetermined with the randomly selected values of the second set of radioparameters; and generate at least one set of output data that is astatistical characterization of the radio network, using at least one ofthe static dataset and the dynamic dataset, to determine at least one ofspectrum availability over all geographic regions and likelihood of aradio being placed on a move list over all geographic regions.

Example 23 includes the computer program product of Example 22, whereinthe determined at least one of spectrum availability over all geographicregions and the determined likelihood of a radio being placed on a movelist over all geographic regions are displayed as a map of geographicregion and using different indicia to indicate a range of thecorresponding availability and a range of the corresponding likelihood.

Example 24 includes the computer program product of any of Examples22-23, wherein the program instructions are further configured, whenexecuted by the at least one programmable processor, to cause the atleast one programmable processor to determine at least one of combinedoverall PAL impairment, static PAL impairment, and dynamic PALimpairment based upon at least one of the determined at least onespectrum availability and the determined likelihood of a radio beingplaced on a move list.

Example 25 includes the computer program product of any of Examples22-24, wherein the at least one of combined overall PAL impairment,static PAL impairment, and dynamic PAL impairment are displayed as a mapof a geographic region and using different indicia to indicate a rangeof the corresponding impairment.

Example 26 includes the computer program product of any of Examples22-25, wherein generating the at least one set of output data comprisesgenerating output data that statistically characterizes parameters ofthe radio network as market penetration increases.

Example 27 includes the computer program product of any of Examples22-26, wherein the number of candidate geographic locations for ageographic region that is a smaller geographic region is determined bymultiplying the number of radios per channel in the geographic region bya scaling factor.

Example 28 includes the computer program product of Example 27, whereinthe scaling factor is based upon a user defined minimum inter-radiodistance.

Example 29 includes the computer program product of any of Examples27-28, wherein a product of the multiplication is either rounded up or afloor mathematical function is applied to the product.

Example 30 includes the computer program product of any of Examples22-29, wherein determining the static data set further comprises:determining if the transmit power level of any radio in the neighborhoodand in the channel is below a threshold power level; and upondetermining that the transmit power level of one or more radios in theneighborhood and in the channel are below the threshold, then determinea radio transmit power level, for a channel and for radios in theneighborhood determined to have a transmit power equal to or greaterthan the threshold power level, that distributes interference marginamongst the determined radios using the randomly selected values of thefirst set of radio parameters.

Example 31 includes the computer program product of any of Examples22-30, wherein the program instructions are further configured, whenexecuted by the at least one programmable processor, to cause the atleast one programmable processor to determine a maximum possibletransmission power at each candidate radio location in the absence ofinterference from other radios; and display a map of a candidate radiolocations in a geographic region using different indicia to indicate arange of the corresponding maximum possible transmission power at eachcandidate radio location.

Example 32 includes the computer program product of any of Examples22-31, wherein the program instructions are further configured, whenexecuted by the at least one programmable processor, to cause the atleast one programmable processor to determine a correlation between atleast one radio parameter and probability of being placed on a movelist, where the at least one radio parameter comprises radio location.

A number of embodiments of the invention defined by the following claimshave been described. Nevertheless, it will be understood that variousmodifications to the described embodiments may be made without departingfrom the spirit and scope of the claimed invention. Accordingly, otherembodiments are within the scope of the following claims.

1. A method of modelling a radio network in a geographic regionutilizing shared spectra, comprising: obtaining population data for thegeographic region; determining a number of radios per channel in thegeographic region, N′; determining candidate geographic location forradios in the geographic region; determining, for every channel in theshared spectra, at least one of a static dataset and a dynamic dataset;wherein the static dataset comprises a set of n*N′*R elements, where nis a number of data types, comprising at least one independent variableand at least one dependent variable, of the static dataset and R is anumber of trials to determine the at least one dependent variable usingrandom values of the at least one independent variable, where the datatypes comprise a first set of radio parameters whose values are randomlyselected and a radio transmit power level for a channel, and where theradio transmit power level for the channel is determined so as todistribute interference margin amongst radios in a neighborhood of aprotection point or an incumbent user using the randomly selected valuesof the first set of radio parameters; wherein the dynamic datasetcomprises a set of l*N′*R elements, where l is a number of data types,comprising at least one independent variable and at least one dependentvariable, of the dynamic data set, where the data types comprise asecond set of radio parameters whose values are randomly selected and amove list of radios, operating in the channel, whose transmissions areterminated upon operation of an incumbent system using the channel, andwhere the move list is determined with the randomly selected values ofthe second set of radio parameters; and generating at least one set ofoutput data that is a statistical characterization of the radio network,using at least one of the static dataset and the dynamic dataset, todetermine at least one of spectrum availability over all geographicregions and likelihood of a radio being placed on a move list over allgeographic regions.
 2. The method of claim 1, wherein the determined atleast one of spectrum availability over all geographic regions and thedetermined likelihood of a radio being placed on a move list over allgeographic regions are displayed as a map of geographic region and usingdifferent indicia to indicate a range of the corresponding availabilityand a range of the corresponding likelihood.
 3. The method of claim 1,further comprising determining at least one of combined overall PALimpairment, static PAL impairment, and dynamic PAL impairment based uponat least one of the determined at least one spectrum availability andthe determined likelihood of a radio being placed on a move list.
 4. Themethod of claim 3, wherein the at least one of combined overall PALimpairment, static PAL impairment, and dynamic PAL impairment aredisplayed as a map of a geographic region and using different indicia toindicate a range of the corresponding impairment.
 5. The method of claim1, wherein generating the at least one set of output data comprisesgenerating output data that statistically characterizes parameters ofthe radio network as market penetration increases.
 6. The method ofclaim 1, wherein the number of candidate geographic locations for ageographic region that is a smaller geographic region is determined bymultiplying the number of radios per channel in the geographic region bya scaling factor.
 7. The method of claim 6, wherein the scaling factoris based upon a user defined minimum inter-radio distance.
 8. The methodof claim 6, wherein a product of the multiplication is either rounded upor a floor mathematical function is applied to the product.
 9. Themethod of claim 1, wherein determining the static data set furthercomprises: determining if the transmit power level of any radio in theneighborhood and in the channel is below a threshold power level; andupon determining that the transmit power level of one or more radios inthe neighborhood and in the channel are below the threshold, thendetermine a radio transmit power level, for a channel and for radios inthe neighborhood determined to have a transmit power equal to or greaterthan the threshold power level, that distributes interference marginamongst the determined radios using the randomly selected values of thefirst set of radio parameters.
 10. The method of claim 1, furthercomprising determining a maximum possible transmission power at eachcandidate radio location in the absence of interference from otherradios; and displaying a map of a candidate radio locations in ageographic region using different indicia to indicate a range of thecorresponding maximum possible transmission power at each candidateradio location.
 11. A system, comprising: processing circuitryconfigured to: obtain population data for the geographic region;determine a number of radios per channel in the geographic region, N′;determining candidate geographic location for radios in the geographicregion; determining, for every channel in the shared spectra, at leastone of a static dataset and a dynamic dataset; wherein the staticdataset comprises a set of n*N′*R elements, where n is a number of datatypes, comprising at least one independent variable and at least onedependent variable, of the static dataset and R is a number of trials todetermine the at least one dependent variable using random values of theat least one independent variable, where the data types comprise a firstset of radio parameters whose values are randomly selected and a radiotransmit power level for a channel, and where the radio transmit powerlevel for the channel is determined so as to distribute interferencemargin amongst radios in a neighborhood of a protection point or anincumbent user using the randomly selected values of the first set ofradio parameters; wherein the dynamic dataset comprises a set of l*N′*Relements, where l is a number of data types, comprising at least oneindependent variable and at least one dependent variable, of the dynamicdata set, where the data types comprise a second set of radio parameterswhose values are randomly selected and a move list of radios, operatingin the channel, whose transmissions are terminated upon operation of anincumbent system using the channel, and where the move list isdetermined with the randomly selected values of the second set of radioparameters; and generate at least one set of output data that is astatistical characterization of the radio network, using at least one ofthe static dataset and the dynamic dataset, to determine at least one ofspectrum availability over all geographic regions and likelihood of aradio being placed on a move list over all geographic regions.
 12. Thesystem of claim 11, wherein the determined at least one of spectrumavailability over all geographic regions and the determined likelihoodof a radio being placed on a move list over all geographic regions aredisplayed as a map of geographic region and using different indicia toindicate a range of the corresponding availability and a range of thecorresponding likelihood.
 13. The system of claim 11, further comprisingdetermine at least one of combined overall PAL impairment, static PALimpairment, and dynamic PAL impairment based upon at least one of thedetermined at least one spectrum availability and the determinedlikelihood of a radio being placed on a move list.
 14. The system ofclaim 11, wherein the at least one of combined overall PAL impairment,static PAL impairment, and dynamic PAL impairment are displayed as a mapof a geographic region and using different indicia to indicate a rangeof the corresponding impairment.
 15. The system of claim 11, whereingenerating the at least one set of output data comprises generatingoutput data that statistically characterizes parameters of the radionetwork as market penetration increases.
 16. The system of claim 11,wherein the number of candidate geographic locations for a geographicregion that is a smaller geographic region is determined by multiplyingthe number of radios per channel in the geographic region by a scalingfactor.
 17. The system of claim 16, wherein the scaling factor is basedupon a user defined minimum inter-radio distance.
 18. The system ofclaim 16, wherein a product of the multiplication is either rounded upor a floor mathematical function is applied to the product.
 19. Thesystem of claim 11, wherein determining the static data set furthercomprises: determining if the transmit power level of any radio in theneighborhood and in the channel is below a threshold power level; andupon determining that the transmit power level of one or more radios inthe neighborhood and in the channel are below the threshold, thendetermine a radio transmit power level, for a channel and for radios inthe neighborhood determined to have a transmit power equal to or greaterthan the threshold power level, that distributes interference marginamongst the determined radios using the randomly selected values of thefirst set of radio parameters.
 20. The method of claim 11, wherein theprocessing circuitry is further configured to determine a maximumpossible transmission power at each candidate radio location in theabsence of interference from other radios; and display a map of acandidate radio locations in a geographic region using different indiciato indicate a range of the corresponding maximum possible transmissionpower at each candidate radio location.
 21. The system of claim 11,wherein the processing circuitry is further configured to determine acorrelation between at least one radio parameter and probability ofbeing placed on a move list, where the at least one radio parametercomprises radio location.
 22. A program product comprising anon-transitory processor readable medium on which program instructionsare embodied, wherein the program instructions are configured, whenexecuted by at least one programmable processor, to cause the at leastone programmable processor to: obtain population data for the geographicregion; determine a number of radios per channel in the geographicregion, N′; determining candidate geographic location for radios in thegeographic region; determining, for every channel in the shared spectra,at least one of a static dataset and a dynamic dataset; wherein thestatic dataset comprises a set of n*N′*R elements, where n is a numberof data types, comprising at least one independent variable and at leastone dependent variable, of the static dataset and R is a number oftrials to determine the at least one dependent variable using randomvalues of the at least one independent variable, where the data typescomprise a first set of radio parameters whose values are randomlyselected and a radio transmit power level for a channel, and where theradio transmit power level for the channel is determined so as todistribute interference margin amongst radios in a neighborhood of aprotection point or an incumbent user using the randomly selected valuesof the first set of radio parameters; wherein the dynamic datasetcomprises a set of l*N′*R elements, where l is a number of data types,comprising at least one independent variable and at least one dependentvariable, of the dynamic data set, where the data types comprise asecond set of radio parameters whose values are randomly selected and amove list of radios, operating in the channel, whose transmissions areterminated upon operation of an incumbent system using the channel, andwhere the move list is determined with the randomly selected values ofthe second set of radio parameters; and generate at least one set ofoutput data that is a statistical characterization of the radio network,using at least one of the static dataset and the dynamic dataset, todetermine at least one of spectrum availability over all geographicregions and likelihood of a radio being placed on a move list over allgeographic regions.
 23. The computer program product of claim 22,wherein the determined at least one of spectrum availability over allgeographic regions and the determined likelihood of a radio being placedon a move list over all geographic regions are displayed as a map ofgeographic region and using different indicia to indicate a range of thecorresponding availability and a range of the corresponding likelihood.24. The computer program product of claim 22, wherein the programinstructions are further configured, when executed by the at least oneprogrammable processor, to cause the at least one programmable processorto determine at least one of combined overall PAL impairment, static PALimpairment, and dynamic PAL impairment based upon at least one of thedetermined at least one spectrum availability and the determinedlikelihood of a radio being placed on a move list.
 25. The computerprogram product of claim 22, wherein the at least one of combinedoverall PAL impairment, static PAL impairment, and dynamic PALimpairment are displayed as a map of a geographic region and usingdifferent indicia to indicate a range of the corresponding impairment.26. The computer program product of claim 22, wherein generating the atleast one set of output data comprises generating output data thatstatistically characterizes parameters of the radio network as marketpenetration increases.
 27. The computer program product of claim 22,wherein the number of candidate geographic locations for a geographicregion that is a smaller geographic region is determined by multiplyingthe number of radios per channel in the geographic region by a scalingfactor.
 28. The computer program product of claim 27, wherein thescaling factor is based upon a user defined minimum inter-radiodistance.
 29. The computer program product of claim 27, wherein aproduct of the multiplication is either rounded up or a floormathematical function is applied to the product.
 30. The computerprogram product of claim 22, wherein determining the static data setfurther comprises: determining if the transmit power level of any radioin the neighborhood and in the channel is below a threshold power level;and upon determining that the transmit power level of one or more radiosin the neighborhood and in the channel are below the threshold, thendetermine a radio transmit power level, for a channel and for radios inthe neighborhood determined to have a transmit power equal to or greaterthan the threshold power level, that distributes interference marginamongst the determined radios using the randomly selected values of thefirst set of radio parameters.
 31. The computer program product of claim22, wherein the program instructions are further configured, whenexecuted by the at least one programmable processor, to cause the atleast one programmable processor to determine a maximum possibletransmission power at each candidate radio location in the absence ofinterference from other radios; and display a map of a candidate radiolocations in a geographic region using different indicia to indicate arange of the corresponding maximum possible transmission power at eachcandidate radio location.
 32. The computer program product of claim 22,wherein the program instructions are further configured, when executedby the at least one programmable processor, to cause the at least oneprogrammable processor to determine a correlation between at least oneradio parameter and probability of being placed on a move list, wherethe at least one radio parameter comprises radio location.